Updated October 2026
At an AI application startup, hire product engineers first, people who can build evaluation sets, work with model APIs and data pipelines, and ship. Add a data engineer next, then someone who owns inference cost, then a forward-deployed engineer once enterprise customers arrive. A research scientist comes later, and only if your advantage is the model itself.
A common and expensive early hire at AI startups is a researcher brought in as employee two or three. Most AI startups win on workflow and data, so a research hire at that stage tends to produce papers and prototypes while the product waits for someone to ship it.
Model company or application company: decide this before the job ad
Know which of these two companies you are building, because the answer changes your first five hires.
Most AI startups sit on the right. Fine-tuning an open model or building retrieval still counts as application work, which a strong engineer with good evals can do. On the left, research talent is your founding team.
Five capabilities to cover before you hire a researcher
Evals
The most useful skill in an early AI engineer is building evaluation sets, meaning real inputs, agreed correct outputs and a score you rerun whenever a prompt or model changes. Without evals, every model upgrade is a guess and every customer complaint becomes an argument.
Data engineering
Your output is only as good as what you feed the model. Someone has to own ingestion, cleaning, chunking and permissions on customer data, and that person is often more valuable than a second model specialist.
Inference cost and platform
Model bills grow with usage, so margins can shrink as you win customers. Give one engineer ownership of cost per request, caching, model routing and latency, with a number they report on. Our guide to hiring ML and AI engineers covers hiring for these roles in depth.
Forward-deployed engineering
Enterprise buyers want the product wired into their systems and tuned to their documents. A forward-deployed or solutions engineer does that work on site or on calls and brings back what the product is missing. See our post on hiring a solutions engineer.
Governance
Someone must own AI governance as a named responsibility: awareness of the EU AI Act, data rights in customer contracts, whether customer data trains your models, and what you tell buyers' security teams. At Seed this is usually a founder working with outside counsel, so check the detail with your lawyer.
Worked example: a Seed-stage AI document review startup for legal teams
Take an illustrative five-person Seed startup reviewing contracts for in-house legal teams and law firms: a lawyer and an engineer as founders, plus three engineers. Here are the next five hires over 12 to 18 months.
The researcher is a conditional hire tied to what the evals show. Governance stays with the lawyer co-founder, because law firm buyers ask about privileged documents and confidentiality on the first call.
Testing candidates when AI tools are everywhere
Banning AI tools in interviews tests a skill nobody uses at work. Allow them and change what you assess: whether the candidate notices when the model is wrong, how they would measure quality across many cases, and whether they understand the cost of what they built.
Where Funded.club fits
We run fixed-fee searches for funded startups across North America, Europe and APAC, and have helped more than 500 companies from Seed to Series D since 2019. One dedicated recruiter handles your search end to end, with first screened candidates within 7 days and 33 days on average from kick-off to hire.
Fees range from $4,900 to $21,900 per hire, averaging 6 to 9% of salary compared with the 20 to 25% contingency recruiters typically charge. As an example, a role paying up to $150,000 costs $11,500. First-time customers can ask to defer the advance until they see their first candidates, and if we miss a shortlist of at least 3 qualified candidates within 30 days, the advance comes back in full. Full bands are on our pricing page.
Frequently asked questions
Should my first AI hire be a machine learning researcher?
Only if your product depends on a model nobody else has. Application-layer startups get more from a product engineer who builds evals and ships, adding research once evals show where existing models fall short.
What is an eval, and why does it matter for hiring?
An eval is a set of real inputs with agreed correct outputs that scores your system every time something changes. Candidates who have built evals before tend to ship reliable AI features faster, so ask them to describe one in detail.
Who owns AI governance at an early-stage startup?
Usually a founder with outside counsel, covering the EU AI Act, how customer data is used and buyer security questions. A dedicated hire comes once enterprise volume justifies it.
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